Systems and methods for identifying personalized vascular implants from patient-specific anatomic data

ABSTRACT

Embodiments include methods of identifying a personalized cardiovascular device based on patient-specific geometrical information, the method comprising acquiring a geometric model of at least a portion of a patient&#39;s vascular system; obtaining one or more geometric quantities of one or more blood vessels of the geometric model of the patient&#39;s vascular system; determining the presence or absence of a pathology characteristic at a location in the geometric model of the patient&#39;s vascular system; generating an objective function defined by a plurality of device variables and a plurality of hemodynamic and solid mechanics characteristics; and optimizing the objective function using computational fluid dynamics and structural mechanics analysis to identify a plurality of device variables that result in desired hemodynamic and solid mechanics characteristics.

PRIORITY

This application claims the benefit of priority from U.S. ProvisionalApplication No. 61/866,758, filed Aug. 16, 2013, which is hereinincorporated by reference in its entirety.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally tomedical imaging and related methods. More specifically, particularembodiments of the present disclosure relate to systems and methods foridentifying personalized vascular devices from patient-specific anatomicimage data.

BACKGROUND

Coronary artery disease may produce coronary lesions in the bloodvessels providing blood to the heart, such as a stenosis (abnormalnarrowing of a blood vessel). As a result, blood flow to the heart maybe restricted. A patient suffering from coronary artery disease mayexperience chest pain, referred to as chronic stable angina duringphysical exertion or unstable angina when the patient is at rest. A moresevere manifestation of disease may lead to myocardial infarction, orheart attack.

Patients suffering from chest pain and/or exhibiting symptoms ofcoronary artery disease may be subjected to one or more tests that mayprovide some indirect evidence relating to coronary lesions. Forexample, noninvasive tests may include electrocardiograms, biomarkerevaluation from blood tests, treadmill tests, echocardiography, singlepositron emission computed tomography (SPECT), and positron emissiontomography (PET). Anatomic data may be obtained noninvasively usingcoronary computed tomographic angiography (CCTA). CCTA may be used forimaging of patients with chest pain and involves using computedtomography (CT) technology to image the heart and the coronary arteriesfollowing an intravenous infusion of a contrast agent.

Typically, cardiologists and other health care professionals analyze oneor both of invasive tests and the above-described noninvasive tests todetermine a suitable intervention for improving a patient'scardiovascular blood flow, when necessary. For example, a cardiologistmay look at the images and, based on certain guidelines and know-how,select an intervention, such as a percutaneous coronary intervention(PCI) (i.e., a “stent”) or coronary arterial bypass graft (CABG) tomodify a patient's vasculature and blood flow. The design of medicalimplants is important for deliverability, long-term durability, andoptimal treatment outcome for each patient. In the past, doctors andimplant designers and manufacturers would evaluate the effectiveness ofan implant design on an entire population of candidates for the implant,such as by using statistical analysis.

However, a need exists for a method for noninvasively assessing andpredicting the effects of different interventions and implants oncoronary anatomy, myocardial perfusion, and coronary artery flow of anindividual patient. Such a method and system may benefit cardiologistswho diagnose and plan treatments for patients with suspected coronaryartery disease. In addition, a need exists for a method to predictcoronary artery flow and myocardial perfusion under conditions thatcannot be directly measured, e.g., exercise, and to predict outcomes ofmedical, interventional, and surgical treatments on coronary arteryblood flow and myocardial perfusion of the individual patient. Inaddition, a need exists to automatically identify an optimal treatmentoption from a plurality of feasible treatment options (e.g., allpossible PCI or CABG options), by analyzing noninvasively assessedcoronary anatomy. Finally, a need exists for systems and methods forautomatically designing, defining, or otherwise identifying a customizedor personalized cardiac implant or other intervention for a specificpatient, by analyzing noninvasively assessed coronary anatomy.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for identifying a personalized cardiovasculardevice based on patient-specific geometrical information. One methodincludes acquiring anatomical image data of at least part of thepatient's vascular system; performing, using a processor, one or both ofcomputational fluid dynamics and structural mechanics analysis on theanatomical image data; and identifying, using the processor, apersonalized cardiovascular device for the patient, based on results ofone or both of the computational fluid dynamics and structural mechanicsanalysis of the anatomical image data.

According to certain additional aspects of the present disclosure, onemethod of identifying a personalized cardiovascular device based onpatient-specific geometrical information, includes: acquiring one ormore physiological parameters of a patient, and anatomical image data ofat least part of the patient's vascular system; performing, using aprocessor, one or both of computational fluid dynamics and structuralmechanics analysis on the physiological parameters and anatomical imagedata; and identifying, using the processor, a personalizedcardiovascular device for the patient, based on results of one or bothof the computational fluid dynamics and structural mechanics analysis ofthe patient's physiological parameters and anatomical image data.

According to certain additional aspects of the present disclosure, onemethod of identifying a personalized cardiovascular device based onpatient-specific geometrical information, includes: acquiring one ormore physiological parameters of a patient, and a geometric model of atleast a portion of the patient's vascular system; obtaining one or moregeometric quantities of one or more coronary arteries of the geometricmodel of the patient's vascular system; determining the presence orabsence of plaque at each of a plurality of locations in the geometricmodel of the patient's vascular system; generating an objective functiondefined by a plurality of device variables and a plurality ofhemodynamic and solid mechanics characteristics; and optimizing theobjective function using computational fluid dynamics and structuralmechanics analysis to identify a plurality of device variables thatresult in desired hemodynamic and solid mechanics characteristics.

According to certain additional aspects of the present disclosure, onemethod of identifying a personalized cardiovascular device based onpatient-specific geometrical information, includes: acquiring, indigital format, image data of a patient's vasculature, and one or moremeasured or estimated physiological or phenotypic parameters of thepatient; generating a patient specific model of at least a portion ofthe patient's vasculature; determining pathology characteristics fromcardiovascular geometry extracted from the patient specific model;defining an objective function for a device based on designconsiderations and one or more estimates of hemodynamic and mechanicalcharacteristics; optimizing the objective function, by perturbingdevices and evaluating the objective function using fluid dynamic orstructural mechanic analyses; and using the optimized objective functionto either (i) select a device from a set of available devices, or (ii)manufacture a desired device.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network fordesigning personalized vascular implants from patient-specific anatomicimage data, according to an exemplary embodiment of the presentdisclosure.

FIG. 2 is a block diagram of an exemplary method for designingpersonalized vascular implants from patient-specific anatomic imagedata, according to an exemplary embodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

A new generation of noninvasive tests have been developed to assessblood flow characteristics. These noninvasive tests use patient imaging(such as CT) to determine a patient-specific geometric model of theblood vessels, which may be used computationally to simulate the bloodflow using computational fluid dynamics (CFD) with appropriatephysiological boundary conditions and parameters. Examples of inputs tothese patient-specific boundary conditions include the patient's bloodpressure, blood viscosity and the expected demand of blood from thesupplied tissue (derived from scaling laws and a mass estimation of thesupplied tissue from the patient imaging).

The present disclosure is directed to noninvasively assessing andpredicting the effects of different interventions and implants oncoronary anatomy, myocardial perfusion, and coronary artery flow of anindividual patient. In addition, the present disclosure is directed toautomatically identifying an optimal treatment option from a pluralityof feasible treatment options (e.g., all possible PCI or CABG options),by analyzing noninvasively assessed coronary anatomy, and automaticallydesigning, defining, or otherwise identifying a customized orpersonalized cardiac implant or other intervention for a specificpatient, by analyzing noninvasively assessed coronary anatomy.

More specifically, the present disclosure is directed to an approach forproviding a service for recommending and/or manufacturing personalizedmedical devices or delivery systems. Specifically, the presentlydisclosed systems and methods receive patient information (e.g., 3Dmedical imaging) and generate a patient-specific geometry of vessels andlocations of pathologies for optimal device design. In one embodiment,the present disclosure includes a method of designing or identifying apersonalized cardiovascular implant based on patient-specificgeometrical information. The method involves acquiring one or morephysiological parameters of a patient, and anatomical image data of atleast part of the patient's vascular system; performing one or both ofcomputational fluid dynamics and structural mechanics analyses on thephysiological parameters and anatomical image data of a patient; anddesigning or identifying a personalized cardiovascular implant for thepatient, based on results of one or both of the computational fluiddynamics and structural mechanics analysis of the patient'sphysiological parameters and anatomical image data. Although the presentdisclosure describes these systems and methods in the context ofcoronary artery disease, the same systems and methods are applicable forother vascular systems beyond the coronary arteries, such as peripheralor cerebral arteries or veins.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system and network for designing personalized vascularimplants from patient-specific anatomic data. Specifically, FIG. 1depicts a plurality of physicians 102 and third party providers 104, anyof whom may be connected to an electronic network 100, such as theInternet, through one or more computers, servers, and/or handheld mobiledevices. Physicians 102 and/or third party providers 104 may create orotherwise obtain images of one or more patients' cardiac and/or vascularsystems. The physicians 102 and/or third party providers 104 may alsoobtain any combination of patient-specific information, such as age,medical history, blood pressure, blood viscosity, etc. Physicians 102and/or third party providers 104 may transmit the cardiac/vascularimages and/or patient-specific information to server systems 106 overthe electronic network 100. Server systems 106 may include storagedevices for storing images and data received from physicians 102 and/orthird party providers 104. Server systems 106 may also includeprocessing devices for processing images and data stored in the storagedevices.

FIG. 2 is a block diagram of an exemplary method for designingpersonalized vascular implants from patient-specific anatomic data,according to an exemplary embodiment of the present disclosure. Themethod of FIG. 2 may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 100.

One embodiment of the present disclosure uses patients' cardiac imagingto derive a patient-specific geometric model of the coronary vessels andplaques, and perform computational fluid dynamics and structuralmechanics analysis to determine the optimal design of medical implantsor delivery systems. Specifically, as shown in FIG. 2, one method 200for designing personalized vascular implants from patient-specificanatomic data may include obtaining image and patient information (step202), such as by acquiring a digital representation (e.g., the memory ordigital storage [e.g., hard drive, network drive] of a computationaldevice such as a computer, laptop, DSP, server, etc.) of patient imagedata and a list of one or more estimates of physiological or phenotypicparameters of the patient, such as blood pressure, blood viscosity,patient age, patient gender, etc. Method 200 may further includegenerating a patient specific model of geometry of at least a portion ofthe patient's vasculature (step 204), such as for blood vessels,myocardium, aorta, valves, plaques, and/or cardiac chambers. Thisgeometry may be represented as a list of points in space (possibly witha list of neighbors for each point) in which the space can be mapped tospatial units between points (e.g., millimeters). Method 200 may furtherinclude determining pathology characteristics in the extractedcardiovascular geometry being targeted for treatment (step 206). Forexample, step 206 may include indicating the presence or absence ofplaque at one or more locations. If plaque exists, a list of one or moremeasurements of coronary plaque composition, burden and location may beobtained or generated. Method 200 may further include defining anobjective function for one or more devices and delivery systems (step208), such as based on design considerations (e.g., shape, fractureresistance, drug distribution) and a list of one or more estimates ofbiophysical hemodynamic and mechanical characteristics, such as tissuestress, vessel injury, drug delivery, uniformity of drug distribution,wall-shear stress, oscillatory shear index, particle residence time,Reynolds number, Womersley number, local flow rate, and turbulentkinetic energy. Method 200 may further include optimizing devices anddelivery systems for the defined objective function (step 210), such asby optimizing specifications of devices and delivery systems based onthe objective functions, and/or perturbing geometry by virtuallydeployed devices from existing designs or using a new design forcomputational fluid dynamics or structural mechanics analysis toevaluate the objective function. Method 200 may further includeoptimizing devices and delivery systems for the defined objectivefunction (step 210), such as by inputting device and/or delivery systemvariables into an optimization algorithm to optimize the device's and/ordelivery system's characteristics. Method 200 may further includerecommending a device and/or delivery system from an inventory orcatalog, or making a personalized or customized device and/or deliverysystem, such as using 3D printing (step 212). For example, step 212 mayinclude choosing an optimal device design from inventory (e.g., a devicecatalog consistent with FIG. 2) using patient-specific geometry andevaluation scores, or determining specifications for a customized deviceand delivery mechanism (e.g., custom order, 3D printing, etc.). Method200 may further include storing the inventory selection or specificationin an electronic storage medium (e.g., hard drive, computer RAM, networkcommunication channel), and/or transmitting the selected device designto a physician, such as over a network.

Method 200 will now be described in more detail below with reference toFIG. 2 and specific exemplary characteristics and exemplary steps. Inone embodiment, step 202 may include obtaining image and patientinformation by acquiring a digital representation (e.g., the memory ordigital storage [e.g., hard drive, network drive] of a computationaldevice such as a computer, laptop, DSP, server, etc.) of a patient'sheart scan and the following estimates of physiological or phenotypicparameters of the patient, including:

-   -   Patient age, gender, height, and weight;    -   Heart rate;    -   Systolic and diastolic blood pressure;    -   Blood properties including: plasma, red blood cells        (erythrocytes), hematocrit, white blood cells (leukocytes) and        platelets (thrombocytes), viscosity, yield stress, etc.;    -   Cardiac function (ejection fraction); and    -   Epicardial fat volume.

In one embodiment, step 204 may include generating a patient specificmodel of geometry for one or more of blood vessels, myocardium, aorta,valves, plaques, and chambers. This geometry may be represented as alist of points in space (possibly with a list of neighbors for eachpoint) in which the space can be mapped to spatial units between points(e.g., millimeters). In one embodiment, the patient-specific model mayinclude the geometry for the patient's ascending aorta, coronary arterytree, myocardium, valves, and/or chambers. This geometry may berepresented as a list of points in space (possibly with a list ofneighbors for each point) in which the space can be mapped to spatialunits between points (e.g., millimeters). This model may be derived byperforming a cardiac CT imaging of the patient in the end diastole phaseof the cardiac cycle. This image then may be segmented manually orautomatically to identify voxels belonging to the aorta and the lumen ofthe coronary arteries. Given a 3D image of coronary vasculature, manymethods exist in the literature for extracting a patient-specific modelof cardiovascular geometry. For example, in one embodiment, serversystems 106 may generate a three-dimensional solid model and/orthree-dimensional mesh based on the received patient-specific anatomicaldata. For example, server systems 106 may generate the three-dimensionalmodel and/or mesh based on any of the techniques described in U.S. Pat.No. 8,315,812 by Taylor et al., which issued on Nov. 20, 2012, theentirety of which is hereby incorporated herein by reference.Inaccuracies in the geometry extracted automatically may be corrected bya human observer who compares the extracted geometry with the images andmakes corrections as needed. Once the voxels are identified, thegeometric model can be derived (e.g., using marching cubes).

For the location targeted for treatment, step 204 may further includeobtaining geometric quantities of coronary arteries from thepatient-specific geometric model, such as, for example, characteristicsof the coronary cross-sectional area, the surface of coronary geometry,the coronary centerline, and coronary deformation. Characteristics ofthe coronary cross-sectional area include, for example, thecross-sectional lumen area along the coronary centerline, such as thedegree of tapering, and any irregularity (or circularity) of thecross-sectional lumen boundary. The degree of tapering may be obtained,for instance, by acquiring sample centerline points in a certaininterval (e.g., twice the diameter of the vessel) and computing a slopeof the linearly-fitted cross-sectional area.

Other characteristics of the coronary cross-sectional area include, forexample, the location, length, and degree of stenotic lesions. Thelocation of stenotic lesions may be obtained, for example, by detectingminima of the cross-sectional area curve. The minima are detected viadetecting locations where the first derivative of the area curve is zeroand the second derivative is positive. The cross-sectional area profileshould be smoothed to avoid detecting artificial peaks. The length ofstenotic lesions may be obtained, for example, by computing the proximaland distal locations from the stenotic lesion where the cross-sectionalarea is recovered. Finally, the degree of stenotic lesions may beevaluated, for example, based on reference values of a smoothedcross-sectional area profile using Fourier smoothing or kernelregression.

Characteristics of the surface of coronary geometry may include, forexample, a three-dimensional surface curvature of geometry, including,for instance, Gaussian function, maximum, minimum, and mean.

Characteristics of the coronary centerline include, for example, thecurvature (bending) and tortuosity (non-planarity) of the coronarycenterline. The curvature may be obtained, for example, by computingFrenet curvature using the following equation:

${\kappa = \frac{{p^{\prime} \times p^{''}}}{{p^{\prime}}^{3}}},$

where p is a coordinate of the centerline,or by computing an inverse of the radius of a circumscribed circle alongthe centerline points. The tortuosity may be obtained, for example, bycomputing Frenet torsion using the following equation:

${\tau = \frac{\left( {p^{\prime} \times p^{''}} \right) \cdot p^{\prime\prime\prime}}{{{p^{\prime} \times p^{''}}}^{2}}},$

where p is a coordinate of the centerline.

Characteristics of coronary deformation may include, for example,distensibility of the coronary artery over a cardiac cycle, thebifurcation angle change over a cardiac cycle, and the curvature changeover a cardiac cycle. This metric, therefore, may require multi-phaseCCTA (e.g., diastole and systole).

In one embodiment, step 206 may include determining pathologycharacteristics in the extracted cardiovascular geometry being targetedfor treatment, by indicating the presence or absence of plaque at one ormore locations. If plaque exists, a list of one or more measurements ofcoronary plaque composition, burden and location may be generated orobtained. For the location targeted for treatment, step 206 may includeobtaining quantities of coronary pathology from the patient-specificgeometric model, including, for example, plaque burden (volume) andtype, such as the intensity of plaque, and adverse plaquecharacteristics of existing plaque, such as the presence of positiveremodeling, the presence of low attenuation plaque, and the presence ofspotty calcification.

In one embodiment, step 208 may include defining the objective functionfor device and delivery system by (a) defining the design variables foroptimization; (b) defining metrics based on design consideration and alist of one or more estimates of biophysical hemodynamic and mechanicalcharacteristics; and (c) defining the objective function based ondefined metrics with weights.

Non-limiting examples of design variables for optimization include, forinstance, the type of stent (e.g., coil, tube, slotted, etc.), stentlength, symmetry, materials, percentage metal coverage, surface finish,tapering/contour/cross-section, strut geometry, and drug content anddose (amount and release rate). Strut geometry may include, for example,shape, thickness, spacing, and number of struts.

Non-limiting examples of metrics based on design consideration and alist of one or more estimates of biophysical hemodynamic and mechanicalcharacteristics include, for example, metrics for device optimizationand metrics for delivery system optimization. Metrics for deviceoptimization may include, for example, characteristics of hemodynamicsand mechanics derived from computational flow dynamics, such as flowmechanics characteristics, solid mechanics characteristics, and clinicalevent characteristics.

Non-limiting examples of flow mechanics characteristics include, forexample, mean wall-shear stress, oscillatory shear index (OSI), particleresidence time, turbulent kinetic energy (TKE), drug delivery, anduniformity of drug distribution. Mean wall-shear stress is defined as

${{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{\overset{\rightarrow}{t_{S}}{dt}}}}},$

wherein. {right arrow over (t_(s))} is the wall shear stress vectordefined as the in-plane component of the surface traction vector. OSI isdefined as

${\frac{1}{2}\left( {1 - \frac{{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{\overset{\rightarrow}{t_{s}}{dt}}}}}{\frac{1}{T_{1} - T_{0}}{\int_{T_{0}}^{T_{1}}{{\overset{\rightarrow}{t_{s}}}{dt}}}}} \right)},$

and is a measure of the uni-directionality of shear stress. Particleresidence time, in turn, is a measure of the time it takes blood to beflushed from a specified fluid domain. Finally, TKE is a measure of theintensity of turbulence associated with eddies in turbulent flow, and ischaracterized by measured root-mean-square velocity fluctuation. TKE canbe normalized by kinetic energy.

Non-limiting examples of solid mechanics characteristics include, forexample, tissue stresses, fracture resistance, flexibility, anddeliverability. Non-limiting examples of clinical event characteristicsinclude, for instance, vessel injury, risk of embolization (coveredstents), risk of restenosis or thrombosis/coagulation, and side branchimpairment.

As noted above, the characteristics of hemodynamics and mechanics arederived from computational flow dynamics. Computer methods to simulateblood flow and structural mechanics have been well-studied, and thesimulation may be accomplished with known techniques. For example, toobtain transient characteristics of blood flow, pulsatile flowsimulation may be performed by using a lumped parameter coronaryvascular model for downstream vasculatures, inflow boundary conditionwith coupling a lumped parameter heart model, and a closed loop model todescribe the intramyocardial pressure variation resulting from theinteractions between the heart and arterial system during the cardiaccycle.

Metrics for delivery system optimization may include, for example,length, flexibility/functionality, pre-shaped guide-wire for orderingsize/length/composition, balloon pressure, and balloon compliance.

Regarding defining the objective function based on defined metrics withweights, weights for each metric may be determined on the basis ofintended design consideration. Non-limiting examples of intended designconsideration include performance, deliverability, and durability.

In one embodiment, step 210 may include optimizing device and deliverysystem for the defined objective function by evaluating an existingdesign from an inventory or catalog and a new design. The evaluationsmay involve, for example, perturbing the geometry of the virtuallydeployed devices to perform a computational fluid dynamics or structuralmechanics analysis in order to evaluate their objective function.

In one embodiment, step 212 may include recommending a personalized orcustomized device or delivery system from an inventory or catalog, ormanufacturing the personalized or customized device or delivery systemusing 3D printing. This can be achieved by (a) saving the patientspecific geometry, the results of the evaluation score, and devicedesigns as a digital representation (e.g., the memory or digital storage[e.g., hard drive, network drive] of a computational device such as acomputer, laptop, DSP, server, etc.); (b) making a recommendation fromthe inventory or catalog, and (c) customizing the design to the patient.

Making the recommendation may include, for example, providing a digitalrepresentation of patient-specific geometry with a recommendation of theoptimal design for the customer so that the customer may select devicesfrom an inventory, such as an existing hospital inventory, or a devicecatalog. In one embodiment, the device or delivery system that has thehighest evaluation score is chosen. In another embodiment, the device ordelivery system that has the most similar characteristics to the designwith the highest evaluation score is chosen.

Customizing the design to the patient may include, for example,providing a digital representation of patient-specific geometry with arecommendation of an optimal design for the customer for manufacturing anew design of device and delivery system. The resulting customizeddesign may be ordered for 3D printing based on the optimal designspecification, which is defined as design variables. Non-limitingexamples of these design variables include, for example, type of stent(e.g., coil, tube, slotted, etc.), length, symmetry, materials,percentage metal coverage, surface finish,tapering/contour/cross-section, and strut geometry, such as shape,thickness, spacing, number of struts, and drug content and dose (amountand release rate). Finally, the output model may be saved to anelectronic storage medium (e.g., hard disk, computer RAM, network drive,etc.).

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-21. (canceled)
 22. A method of identifying a personalizedcardiovascular device based on patient-specific geometrical information,the method comprising: acquiring a geometric model of at least a portionof a patient's vascular system; obtaining a geometric value associatedwith a blood vessel of the geometric model of the patient's vascularsystem; determining a pathology characteristic at a location in thegeometric model of the patient's vascular system; determining one ormore device variables based on the obtained geometric value and thedetermined pathology characteristic; and recommending a device ordelivery system based on the determined one or more device variables.23. The method of claim 22, wherein the device includes an implantabledevice or a device for delivery of an implantable device.
 24. The methodof claim 22, further comprising: generating an objective functiondefined by a plurality of device variables and a plurality ofhemodynamic and solid mechanics characteristics; and recommending thedevice or delivery system further based on the generated objectivefunction.
 25. The method of claim 22, further comprising acquiring oneor more physiological parameters chosen from patient age, gender,height, weight, heart rate, systolic and diastolic blood pressure, bloodproperties, cardiac function, and epicardial fat volume.
 26. The methodaccording to claim 25, wherein the blood properties are chosen from atleast one of plasma, red blood cells, hematocrit, white blood cells,platelets, viscosity, and yield stress.
 27. The method according toclaim 25, wherein the one or more physiological parameters are measuredor estimated.
 28. The method according to claim 22, wherein thegeometric model comprises a patient-specific model of geometry for oneor more of blood vessels, myocardium, aorta, valves, plaques, andchambers.
 29. The method according to claim 24, wherein the objectivefunction is chosen from at least one of patient benefit, cost, safety,and fracture risk.
 30. The method according to claim 22, wherein thedevice variables are chosen from stent type, stent length, symmetry,materials, percentage metal coverage, surface finish, tapering, contour,or cross-section of a stent, strut geometry, and drug content or dose.31. The method according to claim 30, wherein the strut geometrycomprises at least one of shape, thickness, spacing, and number ofstruts.
 32. The method according to claim 24, wherein the hemodynamicand solid mechanics characteristics comprise metrics for deviceoptimization.
 33. The method according to claim 32, wherein the metricsfor device optimization are chosen from flow mechanics characteristics,solid mechanics characteristics, and clinical event characteristics. 34.The method according to claim 24, wherein the hemodynamic and solidmechanics characteristics comprise metrics for delivery systemoptimization.
 35. The method according to claim 34, wherein the metricsfor delivery system optimization are chosen from length, flexibility orfunctionality of the delivery system, shape of guide-wire, balloonpressure, and balloon compliance.
 36. A system for identifying apersonalized cardiovascular device based on patient-specific geometricalinformation, the system comprising: a data storage device storinginstructions for identifying a personalized cardiovascular device basedon patient-specific geometrical information; and a processor configuredto execute the instructions to perform a method including the steps of:acquiring a geometric model of at least a portion of a patient'svascular system; obtaining a geometric value associated with a bloodvessel of the geometric model of the patient's vascular system;determining a pathology characteristic at a location in the geometricmodel of the patient's vascular system; determining one or more devicevariables based on the obtained geometric value and the determinedpathology characteristic; and recommending a device or delivery systembased on the determined one or more device variables.
 37. The system ofclaim 36, wherein the system is further configured for: generating anobjective function defined by a plurality of device variables and aplurality of hemodynamic and solid mechanics characteristics; andrecommending the device or delivery system further based on thegenerated objective function.
 38. The system of claim 36, wherein theobjective function is chosen from at least one of patient benefit, cost,safety, and fracture risk.
 39. A non-transitory computer readable mediumfor use on at least a computer system containing computer-executableprogramming instructions for identifying a personalized cardiovasculardevice based on patient-specific geometrical information, theinstructions being executable by the computer system for performing amethod for identifying a personalized cardiovascular device based onpatient-specific geometrical information, the method comprising:acquiring a geometric model of at least a portion of a patient'svascular system; obtaining a geometric value associated with a bloodvessel of the geometric model of the patient's vascular system;determining a pathology characteristic at a location in the geometricmodel of the patient's vascular system; determining one or more devicevariables based on the obtained geometric value and the determinedpathology characteristic; and recommending a device or delivery systembased on the determined one or more device variables.
 40. Thenon-transitory computer readable medium of claim 39, the method furthercomprising: generating an objective function defined by a plurality ofdevice variables and a plurality of hemodynamic and solid mechanicscharacteristics; and recommending the device or delivery system furtherbased on the generated objective function.
 41. The non-transitorycomputer readable medium according to claim 39, wherein the objectivefunction is chosen from at least one of patient benefit, cost, safety,and fracture risk.